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Module Descriptor School of Computer Science and Statistics

Module CodeST3452
Module NameLinear Models II
Module Short TitleN/a
ECTS5
Semester Taught Michaelmas term
Contact HoursLecture hours: 33Total hours: 33
Module PersonnelLecturing staff: Prof Rozenn Dahyot
Learning Outcomes

When students have successfully completed this module they should be able to: Program, analyse and select the best model for explaining datasets. Interpret output of data analysis performed by a computer statistics package.

Learning Aims

The aim of this module is to learn several mathematical techniques to analyse datasets for the purpose of explaining observed outcomes of experiments. Generalised Linear models (GLMs) are an extension to Standard Linear Regression. This extension is two fold. First, the distribution of the differences between the responses and their fitted values by the model is a member of the Exponential family (e.g. Normal, Poisson, Binomial etc.). Second, the relationship between the (expectation of the) responses and the exploratory variables is not anymore linear, and is chosen amongst several possible link (mathematical) functions. The course will focus on applying GLMs in several case studies using R.

Module Content

Generalized Linear Models Exponential family AIC for model selection Deviance Generalised mixed linear models Multinomial distribution Survival Analysis  With case studies analyzed with the R software

Recommended Reading List
  1. An Introduction to Generalized Linear Models, A. J. Dobson & A. G. Barnett, 3rd Ed
  2. Biostatistical Design and Analysis Using R - A Practical Guide, M. Logan, 2010 (Chp 17)
  3. Generalized Linear Models for Insurance Data, P. de Jong and G. Z. Heller, 2008
  4. Generalized Linear Models with Applications in Engineering and Sciences, R. Myers et al., 2010
Module PrerequisitesBasic Statistics and Mathematics
Assessment Details

Exam: 100%

Module Website
Academic Year of DataN/a